How to Configure Custom AI Agent Teams in Taskade for Sprint Planning and Research

Sprint planning is often the most demanding ceremony in agile development. Product managers, Scrum masters, and engineering leads routinely spend hours transforming vague feature requests into structured sprint backlogs. The manual grind is familiar: scanning technical documentation and competitor benchmarks, translating business intent into formal user stories, authoring rigorous Gherkin acceptance criteria, breaking stories down into engineering sub-tasks, and hand-entering each item into a Kanban board.

While many teams attempt to accelerate this workflow using generic AI chatbots, single-prompt solutions quickly collapse under production requirements. A single prompt lacks persistent team context, mixes conflicting personas, hallucinates engineering boundaries, and dumps raw text that still requires manual card creation.

Taskade solves this challenge by unifying custom autonomous agents, multi-source knowledge bases, multi-agent orchestration, and native Kanban views in a single workspace. This guide provides a step-by-step blueprint for configuring a specialized AI agent team in Taskade to conduct autonomous research, decompose epics into ready-to-work sprint tasks, and populate Kanban boards automatically.

The Multi-Agent Sprint Architecture: Roles and Responsibilities

A reliable multi-agent system does not assign everything to one omnipotent bot. Instead, it mirrors high-performing engineering teams by separating concerns along clean context boundaries. In Taskade, you configure an AI Agent Team composed of four distinct agents:

[ Agile Epic / Feature Goal ]
              │
              ▼
   ┌───────────────────────┐
   │   Sprint Lead Agent   │ ◄─── Agile Governance, Velocity History & DoD
   │     (Orchestrator)    │
   └──────────┬────────────┘
              │ Plans & Delegates
              ▼
   ┌───────────────────────┐
   │    Research Agent     │ ◄─── Web Search, API Docs, Tech Feasibility
   │      (Specialist)     │
   └──────────┬────────────┘
              │ Structured Research Findings
              ▼
   ┌───────────────────────┐
   │ User Story Architect  │ ◄─── INVEST Standard, Persona Profiles, PRDs
   │      (Specialist)     │
   └──────────┬────────────┘
              │ INVEST Stories & Gherkin AC
              ▼
   ┌───────────────────────┐
   │ Task Breakdown & QA   │ ◄─── Architecture Guidelines, Test Frameworks
   │      (Specialist)     │
   └──────────┬────────────┘
              │ Engineering Sub-tasks, Story Points, Tags
              ▼
   ┌───────────────────────┐
   │  Taskade Kanban Board │ ─── Backlog Column (Cards, Due Dates, Checklists)
   └───────────────────────┘

1. Sprint Lead Agent (The Orchestrator)

• Mission: Serves as the team supervisor. It parses incoming business goals, routes requests to specialist agents, verifies deliverables against the team's Definition of Done (DoD), and formats the final outputs.
• Knowledge: Team velocity metrics, agile conventions, sprint duration rules, Definition of Ready (DoR), and Definition of Done (DoD).
• Tools: Agent team orchestration, Taskade project creation.

2. Market & Technical Research Agent (The Specialist)

• Mission: Investigates technical constraints, scans open-source libraries, evaluates competitor feature implementations, and retrieves relevant API documentation.
• Knowledge: Architecture diagrams, technology stack declarations, past technical debt registers.
• Tools: Web Search, Web Scraping, document analysis.

3. User Story & Acceptance Criteria Architect (The Specialist)

• Mission: Converts raw ideas and research dossiers into standard INVEST-compliant user stories (As a... I want to... So that...) with formal Gherkin acceptance criteria (Given... When... Then...).
• Knowledge: Product Requirements Documents (PRDs), user personas, past feature specifications.
• Tools: Internal knowledge retrieval.

4. Task Decomposition & QA Agent (The Specialist)

• Mission: Breaks each validated story into concrete engineering work items (Frontend, Backend, Infrastructure, QA), suggests story point estimations using Fibonacci scales, flags technical dependencies, and specifies automated test conditions.
• Knowledge: Repository structures, coding conventions, testing checklists.
• Tools: File and project management commands.

Step-by-Step Configuration Guide in Taskade

Setting up this autonomous sprint engine takes place directly within your Taskade workspace. Follow these five concrete steps.

Step 1: Create and Specialize Individual Custom AI Agents

Before forming a team, you must create each specialist agent within your workspace so that their instructions and reasoning styles remain distinct:

1. In your Taskade workspace or folder, click the Agents tab in the top navigation bar.
2. Click Create Agent (or select New Agent).
3. Configure the Identity and Persona:
   • Assign an intuitive name and icon (e.g., Sprint Orchestrator, Tech Researcher, User Story Architect, Engineering QA Estimator).
   • Set a clear Role (e.g., Senior Scrum Master & Agile Delivery Lead).
4. Define rigorous System Instructions:
   • For the User Story Architect, add rules such as:

     You are a Principal Agile Analyst. Your sole responsibility is translating feature requests and technical research into INVEST-compliant user stories.
     Always write acceptance criteria in strict Gherkin format (Given / When / Then).
     Highlight edge cases, failure states, and non-functional requirements.
     Do not generate code or technical implementation tasks; hand those off to the Engineering QA Estimator.
     

   • For the Task Decomposition & QA Agent, enforce modular outputs:

     You are a Lead Software Architect and QA Engineer. You receive validated user stories and split them into distinct, non-overlapping engineering tasks.
     Categorize tasks under Frontend, Backend, Database/DevOps, or QA.
     Assign Story Points using the Fibonacci scale (1, 2, 3, 5, 8). If a task exceeds 8 points, flag it for further breakdown.
     List explicit preconditions and regression testing criteria for every task.
     

Step 2: Mount Domain Knowledge Bases into Agent Memory

An AI agent without proprietary knowledge generates generic advice. Taskade allows you to ground each agent using real organizational documents:

1. Open the agent settings and switch to the Knowledge tab.
2. Ingest your source documents based on each agent's role:
   • Sprint Lead: Upload your team's Agile Working Agreement, Definition of Ready / Definition of Done, and summaries of recent sprint retrospective action items.
   • Research Agent: Connect your Tech Stack Specification, architecture blueprints, and links to official documentation or dependency repositories.
   • User Story Architect: Attach current Product Roadmaps, Customer Persona Briefs, and previous approved PRDs.
3. Supported formats include PDF, DOCX, Markdown, plain text, live web URLs, and existing Taskade projects. When new architectural documents are finalized, use the Add Project to Agent Knowledge action in Taskade Automations to keep agent memory current without manual re-uploads.

Step 3: Configure Agent Tools and Execution Permissions

Taskade custom agents can interact with external tools and the live internet, but permissions should be allocated selectively:

1. Open the Tools tab in the agent editor.
2. For the Research Agent, toggle on Web Search and Web Scraping so it can autonomously inspect third-party APIs and libraries.
3. For execution actions that modify external data (e.g., posting notifications to Slack or triggering external webhooks via MCP connectors), enable Human-in-the-Loop confirmation. This ensures agents produce proposals first, requiring your click to execute irreversible changes.
4. Keep tools disabled for pure reasoning agents (like the User Story Architect) to preserve context space and eliminate unnecessary latency.

Step 4: Assemble the AI Team and Select the Orchestration Mode

Once the four agents are configured, aggregate them into a cohesive collaborative unit:

1. Navigate to the AI Teams tab in your workspace sidebar and click Create Team.
2. Select your newly created agents: Sprint Orchestrator, Tech Researcher, User Story Architect, and Engineering QA Estimator.
3. Choose the appropriate Execution / Orchestration Mode:
   • Orchestrate Mode (Recommended for Sprint Planning): The Lead Agent receives your epic prompt, creates an execution plan, delegates research sub-tasks to the Research Agent, passes the findings to the Story Architect, routes the draft to the QA Estimator, and reviews the entire package before presenting the final result.
   • Sequential Mode: Forces a linear chain where output from Agent A becomes the input prompt for Agent B. Ideal when requirements are already mature and need no branching research.
   • Auto Mode: The platform dynamically selects the single best-suited agent for a given question.
   • Everyone Mode: All agents generate simultaneous perspectives—useful for initial risk brainstorming, but too verbose for sprint backlog creation.

Step 5: Connect Agent Outputs to Taskade Kanban Boards

Generating structured text in a chat stream is only half the job. Project managers need ready-to-run cards on their Kanban board:

1. Create a project in Taskade and switch the view to Board (Kanban).
2. Configure your standard agile workflow columns: Sprint Backlog, Ready for Dev, In Progress, Code Review / QA, and Done.
3. Open the project chat panel or trigger an automation using the Ask Agent Team action.
4. Instruct the team to output tasks in Taskade's native hierarchical markdown structure:

   - [ ] [PROJ-101] Implement OAuth2 Google Login
     - Description: Enable users to sign in with Google Workspace accounts.
     - Acceptance Criteria: Given an unauthenticated user, when they click "Sign in with Google", then redirect to Google consent screen.
     - Subtasks:
       - [ ] Backend: Configure OAuth client credentials and callback endpoint #backend #estimate:3
       - [ ] Frontend: Implement Google SSO button in AuthModal #frontend #estimate:2
       - [ ] QA: Verify token expiry handling and error modal #qa #estimate:1
   

5. In Taskade, selecting and converting structured bullet lists into board cards happens natively: sub-bullets automatically convert into card checklist items, tags (#backend, #qa) populate card labels, and assigned estimates map to custom fields.

Concrete Execution Blueprint: From High-Level Epic to Sprint Cards

Here is a practical demonstration of how a project manager kicks off the workflow in the AI Team interface:

The Input Prompt

Project Manager:
"@Sprint Orchestrator Team: We need to plan Sprint 24 for the 'Export to PDF and CSV' feature in our analytics dashboard.
Technical context: Node.js / React stack, PostgreSQL database. Users need client-side and scheduled server-side report generation.
Please run background research on standard memory limits for large PDF generation, structure the user stories with Gherkin criteria, break down engineering sub-tasks with story points, and provide the ready-to-import Kanban list."

Autonomous Multi-Agent Hand-off Flow

1. Step 1 (Lead Decomposes): The Sprint Orchestrator reviews the request against team DoR standards. It generates a three-part agenda and instructs the Tech Researcher to evaluate Node.js PDF rendering options (e.g., Puppeteer headless browser vs. streaming libraries like PDFKit) and memory footprint hazards.
2. Step 2 (Research Gathers Facts): The Tech Researcher executes targeted web and documentation lookups. It reports back:
   • Client-side generation works for dashboards under 50 rows via browser print APIs.
   • Server-side PDF export requires async job workers (e.g., BullMQ) to prevent API timeouts during heavy exports.
   • CSV export should be handled via streaming directly from Postgres cursor to prevent out-of-memory crashes.
3. Step 3 (Story Architect Structures Specs): The User Story Architect takes these findings and writes two core stories:
   • US-1: On-demand CSV export from dashboard table.
   • US-2: Background asynchronous PDF generation with download link notification.
   • It appends complete Gherkin acceptance tests, including timeout handling and empty dataset states.
4. Step 4 (QA Estimator Decomposes Tasks): The Engineering QA Estimator maps these stories into 7 modular sub-tasks, assigns story points (totaling 13 points), and defines test coverage criteria.
5. Step 5 (Lead Verifies and Delivers): The Sprint Orchestrator verifies that all acceptance criteria match the Definition of Ready, formats the final list into hierarchical task blocks, and presents it for manager sign-off.

With one click, the project manager moves the validated cards directly into the Sprint Backlog column.

Architectural Principles and Anti-Patterns

Multi-agent systems can easily become inefficient if configured carelessly. Follow these battle-tested principles when designing agent workflows:

Plan Tiers, Model Selection, and AI Credits

Deploying multi-agent teams involves ongoing AI reasoning. Selecting the right Taskade tier and model configuration ensures consistent performance without unexpected costs:

• Taskade Plan Tiers:
  • Free: Supports up to 2 seats, basic custom agents, and a starter allowance of AI credits. Excellent for personal testing.
  • Pro ($10/month billed annually): 10 seats included with expanded monthly AI credits. Suitable for single agile teams running weekly sprint breakdowns.
  • Business ($25/month billed annually): Unlimited seats, higher monthly AI credits, and multi-agent workflow capabilities. The standard choice for growing product teams.
  • Max ($100/month) & Enterprise ($250/month): High-volume credit pools, priority access to top-tier reasoning models, advanced security (SSO/SCIM), and Bring Your Own Key (BYOK) options.
• Matching Models to Task Complexity:
  • Use TSK-Instant / TSK-Standard for high-volume, bounded tasks such as formatting Gherkin syntax, tagging cards, or preliminary doc summarization. These consume minimal credits.
  • Reserve TSK-Reasoning (or high-capacity reasoning modes) for the Sprint Lead Orchestrator during the initial architectural decomposition and final Definition of Done verification.

Summary: From Manual Administrative Overhead to Strategic Delivery

Manual sprint planning drains agile team energy on administrative formatting rather than architectural problem-solving. By building a dedicated 4-agent team in Taskade:

1. You isolate messy technical research inside specialist agent context windows.
2. You enforce uniform INVEST user stories and Gherkin acceptance criteria across every feature.
3. You produce pre-estimated, tagged engineering cards that sync directly with native Taskade Kanban boards.
4. You preserve complete human governance while cutting sprint preparation time from hours to minutes.

Configure your specialist agents, mount your workspace guidelines into their knowledge bases, and let your Taskade agent team turn sprint ambiguity into execution-ready engineering backlogs.

References

• AI Subagents vs Agent Teams: Claude Code and Beyond (2026)
• Taskade Pricing Plans
• Taskade Help Center